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The International Community Service Seminar on the Application of Artificial Intelligence (AI) for students of SMA Muhammadiyah Program Khusus Kotabarat Surakarta was conducted to enhance digital literacy and provide practical understanding of AI utilization in daily life and learning activities. The program aimed to introduce basic concepts of artificial intelligence, its functions, benefits, and challenges in modern education. Through presentations, interactive discussions, and quizzes, participants were exposed to real-life applications of AI such as digital assistants, personalized learning systems, and time management tools. The seminar involved participants from Indonesia and Malaysia, creating a cross-cultural learning environment that enriched the discussion. Both students and teachers gained improved understanding of how AI can support adaptive and effective learning processes. The activity highlighted the importance of responsible technology use in education. Integrating AI into learning environments was found to improve creativity, efficiency, and engagement among participants while also supporting teachers in administrative and instructional tasks.

This study examines the role of Artificial Intelligence (AI) in enhancing student collaborative learning, with a particular emphasis on AI-driven feedback mechanisms and patterns of student interaction in developing effective collaborative skills. Unlike prior studies, this research highlights the mediating effect of AI-driven feedback on teamwork efficiency and overall learning outcomes in collaborative environments. An explanatory quantitative approach was applied using Partial Least Squares Structural Equation Modeling (PLS-SEM) to ensure robust data analysis. Data were collected from 112 university students who were actively engaged in AI-assisted collaborative learning activities, using a structured online survey instrument. The data were subsequently analyzed using SmartPLS software. The results reveal that AI significantly enhances student interaction (β = 0.534, p < 0.000) and improves problem-solving feedback (β = 0.620, p < 0.000), both of which contribute to significantly strengthening collaborative skills (β = 0.716, p < 0.000). However, the findings also indicate that AI alone does not directly improve collaboration without the support of structured pedagogical design and guidance. Therefore, universities should strategically integrate AI-driven feedback into Learning Management Systems (LMS) and strengthen digital literacy initiatives to optimize the effectiveness and sustainability of AI in collaborative learning contexts.

The rapid advancement of digital technology in higher education has transformed learning and assessment systems, particularly through the adoption of Learning Management Systems (LMS) and Computer-Based Tests (CBT). However, many university students still face challenges in adapting to digital examination environments due to limited technical experience and digital literacy. This Community Service Activity (PKM) aims to enhance students’ academic readiness and digital competence through the implementation of a Moodle-based LMS at Universitas Murni Teguh. The activity was conducted in the form of an interactive workshop, including system introduction, simulation of CBT, guided practice, and discussion sessions involving university students and lecturers. The results showed a significant improvement in students’ understanding of LMS features, confidence in using CBT systems, and ability to manage digital-based examinations effectively. Active participation and engagement during the sessions reflected high enthusiasm and adaptability toward digital learning transformation. This activity demonstrates that the implementation of LMS Moodle is effective in improving academic preparedness and digital literacy among students, and can serve as a sustainable model for strengthening technology-based learning in higher education institutions.

The rapid advancement of digital technology in higher education has transformed learning and assessment systems, particularly through the adoption of Learning Management Systems (LMS) and Computer-Based Tests (CBT). However, many university students still face challenges in adapting to digital examination environments due to limited technical experience and digital literacy. This Community Service Activity (PKM) aims to enhance students’ academic readiness and digital competence through the implementation of a Moodle-based LMS at Universitas Murni Teguh. The activity was conducted in the form of an interactive workshop, including system introduction, simulation of CBT, guided practice, and discussion sessions involving university students and lecturers. The results showed a significant improvement in students’ understanding of LMS features, confidence in using CBT systems, and ability to manage digital-based examinations effectively. Active participation and engagement during the sessions reflected high enthusiasm and adaptability toward digital learning transformation. This activity demonstrates that the implementation of LMS Moodle is effective in improving academic preparedness and digital literacy among students, and can serve as a sustainable model for strengthening technology-based learning in higher education institutions.

Tourism is one of the sectors that plays an important role in boosting economic growth through travel activities and destination exploration. Tourists' preferences for nature-based tourism options, such as mountain hiking or beach tourism, are influenced by various factors, ranging from personal experiences and recreational interests to social characteristics. Therefore, a technology-based approach is needed to predict destination choice tendencies more accurately. As artificial intelligence technology develops, deep learning methods have been widely used in classification processes due to their ability to process large amounts of data and recognize complex patterns. In this study, a Multilayer Perceptron (MLP) model is used to classify tourists' preferences between mountain or beach destinations based on a survey dataset. The research stages include data processing, data splitting using a train-test split, model training, and performance evaluation using accuracy, precision, recall, and F1-score. The test results show that the MLP model is capable of achieving an accuracy rate of 99%, confirming that deep learning methods are effective in automatically mapping tourism preference trends. This research is expected to serve as a basis for the development of more personalized travel destination recommendation systems, as well as to support tourism management in formulating targeted promotional strategies.

Artificial intelligence (AI) is currently a rapidly developing technology in all fields, particularly in finance and the military. This study aims to examine the application of Artificial Intelligence (AI) technology to support financial report analysis and internal control within Indonesian Navy (TNI AL) work units. Along with the development of information technology, AI has the potential to provide innovative solutions to improve efficiency, accuracy, and transparency in state financial management, particularly in a military environment that demands high accountability. The research method used was descriptive qualitative with a case study approach in several work units within the Indonesian Navy. Data were obtained through interviews, observations, and a review of relevant documents and literature. The results indicate that the use of AI, such as machine learning and data analytics, can identify unusual financial transaction patterns, predict potential irregularities, and improve the effectiveness of internal oversight. However, the implementation of this technology still faces challenges, such as limited digital infrastructure, the need for human resource training, and the need for policies that support sustainable digital transformation. This study recommends the gradual and strategic integration of AI as part of the reform of the Indonesian Navy's financial management system.

Waste has become a serious environmental problem in Indonesia, which continues to increase along with population growth. The issue of waste management poses serious challenges for the environment, especially in the process of separating organic and inorganic waste. In the field of computer vision, recognising the type and shape of waste through camera images remains a challenge due to variations in shape, colour, and complex lighting conditions. Therefore, this problem utilises Deep Learning technology, which is expected to be widely applied in Indonesia, especially in large cities with high waste volumes. This study aims to distinguish between organic and inorganic waste using the Convolutional Neural Network (CNN) method based on digital images. The developed CNN model was trained to recognise the visual patterns of each type of waste and tested to measure its accuracy. The test results show that the CNN-based classification system is capable of achieving an accuracy rate of 95%, thus proving the effectiveness of this method in supporting artificial intelligence-based automatic waste sorting systems.

Anesthesiology and emergency care require rapid and accurate clinical decision-making. Artificial intelligence (AI) offers substantial potential to support triage, monitoring, and decision-making in critical and emergency anesthesiology settings. This scoping review maps the use of AI in clinical decision-making and emergency patient management in anesthesiology and identifies existing research gaps. A literature search was conducted in ScienceDirect, PubMed, Cochrane Library, and Google Scholar for articles in Indonesian or English published between 2020 and 2025. Study selection followed Tricco’s scoping review framework, and methodological quality was assessed using Joanna Briggs Institute (JBI) tools. Ten articles met the inclusion criteria. AI was shown to improve triage accuracy and efficiency (predictive accuracy up to 99.1% and reductions in waiting time of around 30%). Machine learning models effectively predicted critical care needs and emergency risk, while AI-based clinical decision support systems (CDSS) enhanced the speed and quality of clinical decisions. Key challenges include data bias, ethical and privacy issues, clinician readiness, and integration with hospital information systems. AI and CDSS have strong potential to improve patient safety and clinical decision-making in emergency anesthesiology. Strengthening AI literacy, supportive regulation, and transparent, context-appropriate predictive models are needed for safe and sustainable implementation.

This study aims to develop a web-based Virtual Assistant to improve the efficiency of academic information services at SMA Negeri 1 Parittiga. The research was motivated by the delays and inaccuracies in information delivery caused by the manual system still used in the school. The system development was carried out using the Research and Development approach with the Waterfall model, which includes the stages of needs analysis, design, implementation, and evaluation. The main technologies used are Natural Language Processing (NLP) and the Long Short-Term Memory (LSTM) machine learning algorithm, which allow the assistant to understand and respond to user questions in natural language in a contextual way. The system architecture uses Flask as the backend, Vue.js as the frontend, and Laravel for administrative data management. The testing results show that the system has an accuracy level of 88.4% in providing correct answers and a user satisfaction level of 92%, surpassing the target success rate of 80%. These findings prove that integrating NLP and LSTM can enhance the system's ability to understand conversational context and speed up the distribution of academic information. The study concludes that a web-based Virtual Assistant is an effective solution for the digitalization of school information services and has the potential to support the implementation of artificial intelligence technology in secondary education in Indonesia.

Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disorder that presents significant diagnostic challenges due to its heterogeneous clinical manifestations and symptom overlap with other neurological conditions. Early and accurate diagnosis is critical for initiating timely interventions and improving patient outcomes. Traditional diagnostic approaches rely heavily on clinical expertise and manual interpretation of neuroimaging data, such as structural MRI, Diffusion Tensor Imaging (DTI), and functional MRI (fMRI), which are inherently time-consuming and prone to interobserver variability. Recent advances in Artificial Intelligence (AI) and Deep Learning (DL) have demonstrated potential for automating neuroimaging analysis, yet existing models often suffer from limited generalizability across modalities and datasets. To address these limitations, we propose a Transformer-augmented deep learning ensemble framework for automated ALS diagnosis using multi-modal neuroimaging data. The proposed architecture integrates Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Vision Transformers (ViTs) to leverage the complementary strengths of spatial, temporal, and global contextual feature representations. An adaptive weighting-based fusion mechanism dynamically integrates modality-specific outputs, enhancing the robustness and reliability of the final diagnosis. Comprehensive preprocessing steps, including intensity normalization, motion correction, and modality-specific data augmentation, are employed to ensure cross-modality consistency. Evaluation using 5-fold cross-validation on a curated multi-modal ALS neuroimaging dataset demon-strates the superior performance of the proposed model, achieving a mean classification accuracy of 94.5% ± 0.7%, precision of 93.9% ± 0.8%, recall of 92.9% ± 0.9%, F1-score of 93.4% ± 0.7%, spec-ificity of 97.4% ± 0.6%, and AUC-ROC of 0.968 ± 0.004. These results significantly outperform baseline CNN models and highlight the potential of transformer-augmented ensembles in complex neurodiagnostic applications. This framework offers a promising tool for clinicians, supporting early and precise ALS detection and enabling more personalized and effective patient management strategies.

The COVID-19 pandemic has exposed fundamental vulnerabilities in global supply chain systems, such as over-reliance on single suppliers and a lack of operational visibility. This has highlighted the urgent need for a new approach to risk management—one that leverages smart technologies. Artificial Intelligence (AI) has emerged as a promising solution, thanks to its capabilities in predictive analytics and adaptive, data-driven decision-making in real time. This study aims to develop an AI-based predictive system framework to enhance the resilience of global supply chains in the face of post-pandemic disruptions. Using the Design Science Research (DSR) methodology, the research designs and evaluates a system that integrates algorithms such as LSTM, Random Forest, Natural Language Processing (NLP), and Reinforcement Learning. It also applies a federated learning approach to ensure data privacy among supply chain partners. The study analyzes over 12,000 data entries from diverse sources, including IoT devices, weather data, demand trends, and social media. The system's effectiveness is evaluated through a combination of quantitative methods (PLS-SEM analysis on 103 respondents) and qualitative methods (interviews with 12 industry executives). The findings show that AI-driven predictive analytics significantly improve supply chain resilience (β = 0.67; p < 0.001), with demand forecasting accuracy increasing by up to 40% and delivery times reduced by 30%. Conceptually, the study contributes by designing a resilient model that integrates real-time visibility, adaptability, and cross-organizational collaborative learning. Unlike traditional approaches focused solely on automation, this framework offers a more holistic solution, addressing key gaps in the literature. The implication is clear: AI is becoming a strategic asset in building sustainable, resilient supply chains amid ongoing global uncertainty.

Mathematical logic is a branch of science that studies the principles of logical reasoning and how to judge the truth of a statement. This article discusses the role of mathematical logic in everyday life, especially in education, technology, and finance. Mathematical logic has been proven to help improve critical, analytical, and systematic thinking skills, making it easier for individuals to solve various problems more effectively and efficiently. In the world of education, mathematical logic supports the development of rational, argumentative, and problem-solving skills, and is an important foundation in the science and mathematics curriculum. Teachers can leverage this logic to encourage students in building consistent reasoning and logical argumentation. In the field of technology, mathematical logic is the foundation in the development of modern computing systems, such as algorithms, programming, artificial intelligence, and data structures. Without mathematical logic, digital systems such as search engines, software, and applications cannot function optimally. In finance, mathematical logic plays a role in budget strategy, investment decision-making, statistical analysis, and rational risk management. With a good understanding of logic, individuals and institutions can make more wise and measurable financial decisions. Although his contribution is large, there are still many people who do not understand the basic concepts of mathematical logic. This can be overcome through more inclusive education, contextual approaches, and interactive learning based on real problems, both in schools and non-formal environments. This study emphasizes the importance of mathematical logic as a foundation in supporting a more rational, efficient, and structured life in today's modern era. Increasing mathematical logic literacy can be a national strategy to create a society that thinks critically, is competitive, and ready to face global challenges.

Artificial Intelligence (AI) has become an essential tool in the world of management for decision-making. This article examines the ways in which AI can be used to improve the quality and speed of decision-making, and how AI can improve the operational efficiency of companies. In addition, this article also examines the challenges and opportunities that companies face in adopting AI.In the rapidly evolving digital era, AI has become an essential component of modern business strategies. Today's managers are often faced with the challenge of analyzing very large and complex volumes of data. To make good and timely decisions, AI offers a potential solution with fast and precise data analysis capabilities.The use of AI in decision-making involves machine learning algorithms and models to efficiently process and analyze large amounts of data. This helps managers gain deeper and more accurate insights, enabling more effective decision-making.

The development of digital technology has brought a significant transformation in the field of education, particularly in learning processes. Traditional teaching methods are now shifting toward more flexible, interactive, and accessible digital systems. This article discusses various strategies for integrating technology into learning in the digital era, including the use of Learning Management Systems (LMS), the application of Artificial Intelligence (AI), and the development of multimedia-based interactive learning content. Furthermore, strengthening digital literacy among teachers and students is a key focus in ensuring the successful implementation of technology in education. This study employs a literature review method by summarizing various scholarly sources and previous research findings. The results indicate that the appropriate application of technology in education can enhance learning effectiveness, promote student independence, and foster 21st-century skills. However, challenges such as unequal access to technology and the lack of teacher training remain obstacles that need to be addressed. Therefore, collaboration between educational institutions, the government, and the community is essential to create an inclusive and sustainable digital learning ecosystem. This article is expected to contribute to the development of adaptive learning strategies aligned with technological advancements and the needs of the times.    

This research is a Systematic Literature Review (SLR) aimed at analyzing the application of Artificial Intelligence (AI) technology in the management of information technology (IT) projects. This study focuses on identifying the AI technologies employed, the benefits gained, and the challenges faced in implementing these technologies. The study gathers and analyzes literature from various leading databases, including Scopus, IEEE Xplore, and SpringerLink, within the timeframe of 2015–2025. The findings reveal that AI technologies such as machine learning, predictive analytics, and natural language processing play a significant role in improving efficiency, reducing risks, and supporting decision-making in IT project management. However, challenges such as data quality, organizational resistance, and implementation costs remain major obstacles in adopting this technology. This review provides comprehensive insights into trends, benefits, and barriers associated with AI utilization, along with recommendations for more effective implementation in the future.

This study aims to examine the application of artificial intelligence technology in the transformation of education management in Junior High Schools to improve learning effectiveness. The research method used is a qualitative approach with a case study design, which involves observation, in-depth interviews with principals, teachers, and students, as well as analysis of documents related to the implementation of artificial intelligence-based digital learning. The data obtained was analyzed using thematic analysis techniques to identify the challenges, opportunities, and impacts of artificial intelligence technology in learning in junior high schools. The results of the study show that the application of artificial intelligence can increase the effectiveness of learning, especially in terms of personalization of learning and management of student learning time. The contribution of this research provides insight into the importance of artificial intelligence technology-based education management in improving the quality of learning at the junior high school level.

This research aims to measure the influence of dependence on the use of artificial intelligence (AI) applications on the learning effectiveness of Islamic Education Management students. Using quantitative research methods, data was collected through questionnaires distributed to 150 students at several universities. Data analysis was carried out using a simple linear regression test to determine the correlation between the dependency variable on AI applications (X) and learning effectiveness (Y). The research results show that 78% of respondents use AI applications in their daily learning activities, with 65% of them feeling more efficient in accessing information. However, there are 40% of students who show decreased motivation for independent learning due to dependence on AI applications. The results of the regression test show that there is a significant positive correlation between the use of AI applications and learning effectiveness with a correlation coefficient of 0.52 and a significance of p < 0.05. These findings indicate that the use of AI plays an important role in increasing learning effectiveness, but also has the potential to reduce motivation for independent learning. It is hoped that the use of AI will be accompanied by learning strategies that support student independence and critical thinking.

The purpose of integrating artificial intelligence (AI) technology into campus smart parking management systems is to improve user comfort, safety, and efficiency. To control car traffic in real-time, the system combines technologies including computer vision, machine learning, and the Internet of Things (IoT). Automatic vehicle detection, license plate recognition, parking lot availability prediction, and ideal vehicle flow regulation are some of the key characteristics. The system can effectively recommend parking to users through a mobile app by using pattern analysis and historical data. The implementation results showed a 25% reduction in traffic in the university's parking area and a 30% increase in parking time efficiency. According to the findings of the study, applying AI technology in parking management can be a creative way to overcome the difficulties associated with facility management in higher education environments.

Curriculum administration is all business processes that have been planned and attempted deliberately and earnestly as well as continuous guidance on teaching and learning activities effectively and efficiently in order to achieve the educational goals that have been set. The importance of curriculum administration in educational institutions, which includes the selection of materials, teaching methods, and assessments that are in accordance with educational goals and student needs. Curriculum administration plays a role in ensuring that the curriculum is well structured and organized through various levels of planning, implementation, and supervision. Curriculum evaluation is also an important part of education management, aiming to collect information about the effectiveness of curriculum implementation and the impact of learning outcomes. In addition, this journal highlights the role of teachers in curriculum administration as implementers, adapters, developers, and researchers. The presence of artificial intelligence (AI) technology presents an opportunity for educators to carry out a learning process that focuses on the needs, interests, and learning styles of students, because the independent curriculum requires educators to carry out differentiated learning as an initiative in facilitating students, especially in Pancasila education, which incidentally is still carried out by conventional models or methods.

The application of artificial intelligence in education has brought significant changes in learning methods and classroom management. This paper discusses the Application of AI for Learning training held for lecturers at UDB Surakarta with the aim of improving understanding and skills in the application of AI technology. The training method includes a combination of theoretical and practical sessions. The theory session ran in several stages, namely a discussion on the introduction of AI, demonstrations of AI tools and technologies such as ChatGPT, Copilot, and Gemini. Practices of using AI in the creation of learning materials, assessment of learning outcomes, and writing scientific articles. The training, which was attended by 12 lecturers, provided hands-on experience in the application of AI in the world of education. The results of the training showed a significant improvement in the understanding and skills of the participants, with 91.67% of participants feeling satisfied and hoping for similar training on a regular basis. The training evaluation includes an analysis of participant feedback that shows that the training is helpful in preparing for learning, creating teaching modules, and other learning tools. The evaluation of the training results leads to the conclusion that this training is effective in improving lecturers' competence in using AI, encouraging creativity in learning, and is expected to increase effectiveness in learning.